Changepoint Detection in Time Series Data Shift Analysis
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Solution Overview
Problem
Organizations face challenges in analyzing vast amounts of data to uncover insights about changes within the data, as current methods require significant resources and are unsustainable due to the rapid pace of new data arrival.
Innovation Solution
A data shift determination system that receives a first model trained to detect change points in a time series dataset, generates a second corresponding model for each change point, and assigns a severity metric based on performance differences between the models to prioritize and determine the causes of change points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed review of data is performed to uncover insights about changes, then measurement precision is improved, but productivity deteriorates due to unsustainable resource requirements
Solution Approach 1:
The patent segments the data review process by introducing multiple specialized models (first model for initial change point detection, second model for cause determination) that divide the analytical workload. This segmentation allows automated processing of large datasets while maintaining precision in identifying significant changes and their causes.
Solution Approach 2:
The patent introduces automated modeling systems as intermediaries between raw data and human analysis. These models act as mediators that process data, detect change points, and generate insights, thereby enhancing productivity without sacrificing measurement precision in identifying data shifts and their causes.
2Measurement precision
If comprehensive analysis of all change points is performed, then measurement precision is improved, but loss of time increases due to the volume of data requiring processing
Solution Approach 1:
The patent segments the analysis by using the first model to detect change points and the second model to determine their causes, processing different aspects separately and in sequence. This reduces the time required for comprehensive analysis while maintaining precision in both detection and cause determination.
Solution Approach 2:
The first model performs preliminary detection of change points before the second model analyzes their causes. This preliminary action filters the data to focus only on significant changes, reducing the overall analysis time while maintaining measurement precision.
3Productivity
If automated modeling systems are used to process data at higher pace, then productivity is improved, but device complexity increases due to multiple models and processing steps
Solution Approach 1:
The patent segments the automated processing system into distinct first and second models with specialized functions. This segmentation manages complexity by creating modular, independently manageable components that together achieve high productivity in data processing.
Solution Approach 2:
The modeling system is designed with multi-functionality, where the first model detects change points and the second model determines their causes. This universal approach handles multiple analytical tasks within a unified automated framework, improving productivity while organizing complexity through functional integration.
Data Source
AI summary
Methods and systems are described herein for determining data shifts using change point detection in time series datasets. The system receives, from a change point modelling system, a first model and a plurality of change points detected within a time series dataset using the first model. The system generates, for each change point, a second corresponding model that fails to detect a corresponding change point and inputs the time series dataset into the first model and into each second corresponding model. The system determines a performance difference between the first model and each second corresponding model and assigns severity metrics to the change points based on the corresponding performance difference. The system selects a subset of change points based on the severity metrics and determines a cause of each change point of the subset of change points.